记忆电阻器
材料科学
铯
铅(地质)
卤化物
纳米技术
非易失性存储器
光电子学
相变存储器
电气工程
工程类
化学
图层(电子)
地质学
地貌学
无机化学
作者
Tsung‐Kai Su,Wei‐Chen Cheng,Cheng‐Yueh Chen,Wei‐Chun Wang,Yung‐Tang Chuang,Guang‐Hsun Tan,Hao‐Cheng Lin,Cheng‐Hung Hou,Ching-Min Liu,Yun‐Ting Chang,Jing‐Jong Shyue,Kai–Chiang Wu,Huey-Wen Lin
出处
期刊:ACS Nano
[American Chemical Society]
日期:2022-07-11
卷期号:16 (8): 12979-12990
被引量:22
标识
DOI:10.1021/acsnano.2c05436
摘要
Recently, conductive-bridging memristors based on metal halides, such as halide perovskites, have been demonstrated as promising components for brain-inspired hardware-based neuromorphic computing. However, realizing devices that simultaneously fulfill all of the key merits (low operating voltage, high dynamic range, multilevel nonvolatile storage capability, and good endurance) remains a great challenge. Herein, we describe lead-free cesium halide memristors incorporating a MoOX interfacial layer as a type of conductive-bridging memristor. With this design, we obtained highly uniform and reproducible memristors that exhibited all-around resistive switching characteristics: ultralow operating voltages (<0.18 V), low variations (<30 mV), long retention times (>106 s), high endurance (>105, full on/off cycles), record-high on/off ratios (>1010, smaller devices having areas <5 × 10-4 mm2), fast switching (<200 ns), and multilevel programming abilities (>64 states). With these memristors, we successfully implemented stateful logic functions in a reconfigurable architecture and accomplished a high classification accuracy (ca. 90%) in the simulated hand-written-digits classification task, suggesting their versatility in future in-memory computing applications. In addition, we exploited the room-temperature fabrication of the devices to construct a fully functional three-dimensional stack of memristors, which demonstrates their potential of high-density integration desired for data-intensive neuromorphic computing. High-performance, environmentally friendly cesium halide memristors provide opportunities toward next-generation electronics beyond von Neumann architectures.
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